English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In recent times, non-intrusive load monitoring (NILM) has emerged as animportant tool for distribution-level energy management systems owing to itspotential for energy conservation and management. However, load monitoring insmart building environments is challenging due to high variability of real-timeload and varied load composition. Furthermore, as the volume and dimensionalityof smart meters data increases, accuracy and computational time are keyconcerning factors. In view of these challenges, this paper proposes animproved NILM technique using multi-head (Mh-Net) convolutional neural network(CNN) under dynamic grid voltage conditions. An attention layer is introducedinto the proposed CNN model, which helps in improving estimation accuracy ofappliance power consumption. The performance of the developed model has beenverified on an experimental laboratory setup for multiple appliance sets withvaried power consumption levels, under dynamic grid voltages. Moreover, theeffectiveness of the proposed model has been verified on widely used UK-DALEdata, and its performance has been compared with existing NILM techniques.Results depict that the proposed model accurately identifies appliances, powerconsumptions and their time-of-use even during practical dynamic grid voltageconditions.

A Multi-Head Convolutional Neural Network Based Non-Intrusive Load Monitoring Algorithm Under Dynamic Grid Voltage Conditions